921 research outputs found
Perspective: network-guided pattern formation of neural dynamics
The understanding of neural activity patterns is fundamentally linked to an
understanding of how the brain's network architecture shapes dynamical
processes. Established approaches rely mostly on deviations of a given network
from certain classes of random graphs. Hypotheses about the supposed role of
prominent topological features (for instance, the roles of modularity, network
motifs, or hierarchical network organization) are derived from these
deviations. An alternative strategy could be to study deviations of network
architectures from regular graphs (rings, lattices) and consider the
implications of such deviations for self-organized dynamic patterns on the
network. Following this strategy, we draw on the theory of spatiotemporal
pattern formation and propose a novel perspective for analyzing dynamics on
networks, by evaluating how the self-organized dynamics are confined by network
architecture to a small set of permissible collective states. In particular, we
discuss the role of prominent topological features of brain connectivity, such
as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the
notion of network-guided pattern formation with numerical simulations and
outline how it can facilitate the understanding of neural dynamics
Critical dynamics on a large human Open Connectome network
Extended numerical simulations of threshold models have been performed on a
human brain network with N=836733 connected nodes available from the Open
Connectome project. While in case of simple threshold models a sharp
discontinuous phase transition without any critical dynamics arises, variable
thresholds models exhibit extended power-law scaling regions. This is
attributed to fact that Griffiths effects, stemming from the
topological/interaction heterogeneity of the network, can become relevant if
the input sensitivity of nodes is equalized. I have studied the effects effects
of link directness, as well as the consequence of inhibitory connections.
Non-universal power-law avalanche size and time distributions have been found
with exponents agreeing with the values obtained in electrode experiments of
the human brain. The dynamical critical region occurs in an extended control
parameter space without the assumption of self organized criticality.Comment: 7 pages, 6 figures, accepted version to appear in PR
Griffiths phases and localization in hierarchical modular networks
We study variants of hierarchical modular network models suggested by Kaiser
and Hilgetag [Frontiers in Neuroinformatics, 4 (2010) 8] to model functional
brain connectivity, using extensive simulations and quenched mean-field theory
(QMF), focusing on structures with a connection probability that decays
exponentially with the level index. Such networks can be embedded in
two-dimensional Euclidean space. We explore the dynamic behavior of the contact
process (CP) and threshold models on networks of this kind, including
hierarchical trees. While in the small-world networks originally proposed to
model brain connectivity, the topological heterogeneities are not strong enough
to induce deviations from mean-field behavior, we show that a Griffiths phase
can emerge under reduced connection probabilities, approaching the percolation
threshold. In this case the topological dimension of the networks is finite,
and extended regions of bursty, power-law dynamics are observed. Localization
in the steady state is also shown via QMF. We investigate the effects of link
asymmetry and coupling disorder, and show that localization can occur even in
small-world networks with high connectivity in case of link disorder.Comment: 18 pages, 20 figures, accepted version in Scientific Report
Hierarchy and Dynamics of Neural Networks
Contains fulltext :
88364.pdf (publisher's version ) (Open Access
From Caenorhabditis elegans to the Human Connectome: A Specific Modular Organisation Increases Metabolic, Functional, and Developmental Efficiency
The connectome, or the entire connectivity of a neural system represented by
network, ranges various scales from synaptic connections between individual
neurons to fibre tract connections between brain regions. Although the
modularity they commonly show has been extensively studied, it is unclear
whether connection specificity of such networks can already be fully explained
by the modularity alone. To answer this question, we study two networks, the
neuronal network of C. elegans and the fibre tract network of human brains
yielded through diffusion spectrum imaging (DSI). We compare them to their
respective benchmark networks with varying modularities, which are generated by
link swapping to have desired modularity values but otherwise maximally random.
We find several network properties that are specific to the neural networks and
cannot be fully explained by the modularity alone. First, the clustering
coefficient and the characteristic path length of C. elegans and human
connectomes are both higher than those of the benchmark networks with similar
modularity. High clustering coefficient indicates efficient local information
distribution and high characteristic path length suggests reduced global
integration. Second, the total wiring length is smaller than for the
alternative configurations with similar modularity. This is due to lower
dispersion of connections, which means each neuron in C. elegans connectome or
each region of interest (ROI) in human connectome reaches fewer ganglia or
cortical areas, respectively. Third, both neural networks show lower
algorithmic entropy compared to the alternative arrangements. This implies that
fewer rules are needed to encode for the organisation of neural systems
Resolving Structure in Human Brain Organization: Identifying Mesoscale Organization in Weighted Network Representations
Human brain anatomy and function display a combination of modular and
hierarchical organization, suggesting the importance of both cohesive
structures and variable resolutions in the facilitation of healthy cognitive
processes. However, tools to simultaneously probe these features of brain
architecture require further development. We propose and apply a set of methods
to extract cohesive structures in network representations of brain connectivity
using multi-resolution techniques. We employ a combination of soft
thresholding, windowed thresholding, and resolution in community detection,
that enable us to identify and isolate structures associated with different
weights. One such mesoscale structure is bipartivity, which quantifies the
extent to which the brain is divided into two partitions with high connectivity
between partitions and low connectivity within partitions. A second,
complementary mesoscale structure is modularity, which quantifies the extent to
which the brain is divided into multiple communities with strong connectivity
within each community and weak connectivity between communities. Our methods
lead to multi-resolution curves of these network diagnostics over a range of
spatial, geometric, and structural scales. For statistical comparison, we
contrast our results with those obtained for several benchmark null models. Our
work demonstrates that multi-resolution diagnostic curves capture complex
organizational profiles in weighted graphs. We apply these methods to the
identification of resolution-specific characteristics of healthy weighted graph
architecture and altered connectivity profiles in psychiatric disease.Comment: Comments welcom
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
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